AlphaMat: a material informatics hub connecting data, features, models and applications

• Published in: npj computational materials Vol. 9; no. 1; pp. 130 - 9
• Main Authors: Wang, Zhilong, Chen, An, Tao, Kehao, Cai, Junfei, Han, Yanqiang, Gao, Jing, Ye, Simin, Wang, Shiwei, Ali, Imran, Li, Jinjin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26.07.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034/1035; 639/301/1034/1037; Artificial intelligence; Bulk modulus; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Computer science; Conductivity; Data processing; Design; Efficiency; Energy; Energy gap; Experiments; Free energy; Heat of formation; Informatics; Information technology; Interdisciplinary subjects; Ion currents; Machine learning; Materials information; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Proprietary; Research methodology; Simulation; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-023-01086-5

The development of modern civil industry, energy and information technology is inseparable from the rapid explorations of new materials. However, only a small fraction of materials being experimentally/computationally studied in a vast chemical space. Artificial intelligence (AI) is promising to address this gap, but faces many challenges, such as data scarcity and inaccurate material descriptors. Here, we develop an AI platform, AlphaMat, that can complete data preprocessing and downstream AI models. With high efficiency and accuracy, AlphaMat exhibits strong powers to model typical 12 material attributes (formation energy, band gap, ionic conductivity, magnetism, bulk modulus, etc.). AlphaMat’s capabilities are further demonstrated to discover thousands of new materials for use in specific domains. AlphaMat does not require users to have strong programming experience, and its effective use will facilitate the development of materials informatics, which is of great significance for the implementation of AI for Science (AI4S).